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    Rights statement: This is the author’s version of a work that was accepted for publication in Applied Soft Computing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Applied Soft Computing, 77, 2019 DOI: 10.1016/j.asoc.2019.01.005

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Self-boosting first-order autonomous learning neuro-fuzzy systems

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Self-boosting first-order autonomous learning neuro-fuzzy systems. / Gu, Xiaowei; Angelov, Plamen Parvanov.

In: Applied Soft Computing, Vol. 77, 01.04.2019, p. 118-134.

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@article{fbb5cf8c5dd048aabaac170ebdeaf341,
title = "Self-boosting first-order autonomous learning neuro-fuzzy systems",
abstract = "In this paper, a detailed mathematical analysis of the optimality of the premise and consequent parts of the recently introduced first-order Autonomous Learning Multi-Model (ALMMo) neuro-fuzzy system is conducted. A novel self-boosting algorithm for structure- and parameter- optimization is, then, introduced to the ALMMo, which results in the self-boosting ALMMo (SBALMMo) neuro-fuzzy system. By minimizing the objective functions with the previously collected data, the SBALMMo is able to optimize its system structure and parameters in few iterations. Numerical examples based benchmark datasets and real-world problems demonstrate the effectiveness and validity of the SBALMMo, and show the strong potential of the proposed approach for real applications.",
keywords = "Autonomous learning, Local optimality, Neuro-fuzzy systems, Self-boosting, Streaming data processing",
author = "Xiaowei Gu and Angelov, {Plamen Parvanov}",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Applied Soft Computing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Applied Soft Computing, 77, 2019 DOI: 10.1016/j.asoc.2019.01.005",
year = "2019",
month = apr,
day = "1",
doi = "10.1016/j.asoc.2019.01.005",
language = "English",
volume = "77",
pages = "118--134",
journal = "Applied Soft Computing",
issn = "1568-4946",
publisher = "Elsevier Science B.V.",

}

RIS

TY - JOUR

T1 - Self-boosting first-order autonomous learning neuro-fuzzy systems

AU - Gu, Xiaowei

AU - Angelov, Plamen Parvanov

N1 - This is the author’s version of a work that was accepted for publication in Applied Soft Computing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Applied Soft Computing, 77, 2019 DOI: 10.1016/j.asoc.2019.01.005

PY - 2019/4/1

Y1 - 2019/4/1

N2 - In this paper, a detailed mathematical analysis of the optimality of the premise and consequent parts of the recently introduced first-order Autonomous Learning Multi-Model (ALMMo) neuro-fuzzy system is conducted. A novel self-boosting algorithm for structure- and parameter- optimization is, then, introduced to the ALMMo, which results in the self-boosting ALMMo (SBALMMo) neuro-fuzzy system. By minimizing the objective functions with the previously collected data, the SBALMMo is able to optimize its system structure and parameters in few iterations. Numerical examples based benchmark datasets and real-world problems demonstrate the effectiveness and validity of the SBALMMo, and show the strong potential of the proposed approach for real applications.

AB - In this paper, a detailed mathematical analysis of the optimality of the premise and consequent parts of the recently introduced first-order Autonomous Learning Multi-Model (ALMMo) neuro-fuzzy system is conducted. A novel self-boosting algorithm for structure- and parameter- optimization is, then, introduced to the ALMMo, which results in the self-boosting ALMMo (SBALMMo) neuro-fuzzy system. By minimizing the objective functions with the previously collected data, the SBALMMo is able to optimize its system structure and parameters in few iterations. Numerical examples based benchmark datasets and real-world problems demonstrate the effectiveness and validity of the SBALMMo, and show the strong potential of the proposed approach for real applications.

KW - Autonomous learning

KW - Local optimality

KW - Neuro-fuzzy systems

KW - Self-boosting

KW - Streaming data processing

U2 - 10.1016/j.asoc.2019.01.005

DO - 10.1016/j.asoc.2019.01.005

M3 - Journal article

AN - SCOPUS:85060475708

VL - 77

SP - 118

EP - 134

JO - Applied Soft Computing

JF - Applied Soft Computing

SN - 1568-4946

ER -